In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas.
이 강좌는 Machine Learning Engineering for Production (MLOps) 특화 과정의 일부입니다.
제공자:


이 강좌에 대하여
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
배울 내용
Identify responsible data collection for building a fair ML production system.
Implement feature engineering, transformation, and selection with TensorFlow Extended
Understand the data journey over a production system’s lifecycle and leverage ML metadata and enterprise schemas to address quickly evolving data.
귀하가 습득할 기술
- ML Metadata
- Convolutional Neural Network
- TensorFlow Extended (TFX)
- Data Validation
- Data transformation
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
제공자:

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
강의 계획표 - 이 강좌에서 배울 내용
Week 1: Collecting, Labeling and Validating Data
This week covers a quick introduction to machine learning production systems. More concretely you will learn about leveraging the TensorFlow Extended (TFX) library to collect, label and validate data to make it production ready.
Week 2: Feature Engineering, Transformation and Selection
Implement feature engineering, transformation, and selection with TensorFlow Extended by encoding structured and unstructured data types and addressing class imbalances
Week 3: Data Journey and Data Storage
Understand the data journey over a production system’s lifecycle and leverage ML metadata and enterprise schemas to address quickly evolving data.
Week 4 (Optional): Advanced Labeling, Augmentation and Data Preprocessing
Combine labeled and unlabeled data to improve ML model accuracy and augment data to diversify your training set.
검토
- 5 stars63.61%
- 4 stars20.08%
- 3 stars9.37%
- 2 stars5.58%
- 1 star1.33%
MACHINE LEARNING DATA LIFECYCLE IN PRODUCTION 의 최상위 리뷰
Best course for the professionals looking to upgrade there ML skills at production level! Thanks to the brilliant and wonderful course instructor.
Lessons are well structured and clear, and the labs are very instructive. Above all the course is fun!
instruction on debugging jupyter and submission issue is important for learners
Very good training about data lifecycle for ML projects
Machine Learning Engineering for Production (MLOps) 특화 과정 정보
Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well.

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